It has been known for many years to build up files, and to provide databases that contain the files, relating to a variety of businesses about whom inquiries are to be made. Many companies conduct investigations aimed at establishing credit worthiness, or lack thereof, of such businesses, and also to associate this credit information with a variety of other data about those businesses.
Besides the more or less traditional investigations into credit worthiness and other related data, there has been developed a novel scheme that will aid customers to determine whether businesses that are inquired about appear to share certain proclivities with businesses already identified as having engaged in questionable, even unethical, activity. In this connection, reference may be made to co-pending application U.S. patent application Ser. No. 10/021,253, filed on Dec. 13, 2001, which is incorporated by reference in its entirety, and that produces by its system's operation a figure of merit or “higher risk” score. In accordance with that invention, the characteristics of certain businesses on file, which businesses are considered to have engaged in questionable activities, are used to train a neural network whereby a neural network module, which is capable of identifying patterns in the data elements or characteristics of those businesses, is created. Thereafter, the data elements of business under inquiry is analyzed by the neural network module such that a weighted sum of its elements or characteristics can be obtained. The so-called “higher risk score” can be developed from this weighted sum. The higher risk score indicates to the subscriber or customer that the business is under financial stress and thus they may want to be careful in extending credit because they may not be repaid.
The present invention is directed to a method of converting such previously developed or stored business data into usable data that may be retrieved by a user in a report format on-the-fly.